OpenDiscourse MCP

# OpenDiscourse MCP Servers - Project Summary ## Project Overview **Project Name**: OpenDiscourse MCP Servers **Version**: 1.0.0 (Initial Development) **Status**: Planning & Architecture Phase **Last Updated**: 2025-12-11 ## Executive Summary The OpenDiscourse MCP Servers project aims to create two robust, production-ready MCP (Model Context Protocol) servers for ingesting and processing bulk government data from [congress.gov](https://congress.gov) and [govinfo.gov](https://govinfo.gov). These servers will provide standardized interfaces for AI systems to access comprehensive U.S. government data. ## Project Goals ### Primary Objectives 1. **Create Two Separate MCP Servers**: - Congress.gov MCP Server (for legislative data) - GovInfo.gov MCP Server (for government publications) 2. **Provide Multiple Distribution Channels**: - PyPI (Python Package Index) - NPM (Node Package Manager) - Docker Hub (Containerized deployment) - UVX (Universal Package Manager) 3. **Ensure Production Readiness**: - Configurable and flexible architecture - Secure secret management - Comprehensive error handling - Performance optimization 4. **Support Multiple Deployment Environments**: - Local development - Cloud deployment - Containerized environments - Serverless architectures ## Technical Architecture ### System Components ``` ┌───────────────────────────────────────────────────────┐ │ OpenDiscourse MCP Servers │ ├───────────────────────────────────────────────────────┤ │ │ │ ┌─────────────────┐ ┌─────────────────┐ │ │ │ Congress.gov │ │ GovInfo.gov │ │ │ │ MCP Server │ │ MCP Server │ │ │ └─────────────────┘ └─────────────────┘ │ │ │ │ ┌───────────────────────────────────────────────┐ │ │ │ Shared Components │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌───────┐ │ │ │ │ │ Config │ │ Logging │ │ ... │ │ │ │ │ └─────────────┘ └─────────────┘ └───────┘ │ │ │ └───────────────────────────────────────────────┘ │ │ │ └───────────────────────────────────────────────────────┘ ``` ### Technology Stack **Core Technologies**: - **Python 3.8+**: Primary implementation language - **Pydantic**: Data validation and settings management - **MCP Protocol**: Standardized AI tool interface - **TypeScript**: For NPM package distribution **Deployment Options**: - **Docker**: Containerized deployment - **PyPI**: Python package distribution - **NPM**: JavaScript/TypeScript distribution - **Direct Installation**: From source code **Data Sources**: - **Congress.gov API**: Legislative data - **GovInfo.gov API**: Government publications - **ProPublica Congress API**: Additional legislative data (optional) ## Project Scope ### In Scope - ✅ Two separate MCP servers with distinct logic - ✅ Bulk data ingestion from government APIs - ✅ Data processing and normalization - ✅ Configuration management system - ✅ Multiple distribution channels - ✅ Comprehensive documentation - ✅ Production-ready error handling - ✅ Performance optimization ### Out of Scope (Future Enhancements) - ❌ Web-based dashboard (future) - ❌ Advanced analytics with ML (future) - ❌ Real-time data streaming (future) - ❌ Mobile applications (future) ## Key Features ### Congress.gov MCP Server - **Legislation Data**: Bills, resolutions, amendments - **Member Information**: Congressional member profiles - **Committee Data**: Committee activities and membership - **Voting Records**: Roll call votes and voting history - **Hearings & Meetings**: Committee hearings data ### GovInfo.gov MCP Server - **Federal Register**: Government publications - **Congressional Record**: Daily proceedings - **Presidential Documents**: Executive orders - **Regulatory Documents**: Federal regulations - **Court Opinions**: Judicial decisions ### Shared Features - **Global Configuration**: Centralized settings management - **Concurrent Processing**: Multiple simultaneous connections - **Error Recovery**: Automatic retry and recovery - **Comprehensive Logging**: Detailed operational logs ## Development Methodology ### Development Approach - **Modular Architecture**: Separate components for each server - **Test-Driven Development**: Comprehensive test coverage - **Continuous Integration**: Automated testing and deployment - **Documentation-First**: Complete documentation before implementation ### Quality Assurance - **Code Reviews**: Peer review process - **Automated Testing**: Unit, integration, and end-to-end tests - **Performance Testing**: Load and stress testing - **Security Audits**: Regular security assessments ### Project Phases 1. **Phase 1: Planning & Architecture** (Current) - Requirements gathering - Technical design - Documentation creation 2. **Phase 2: Core Development** - Implement shared components - Develop Congress.gov server - Develop GovInfo.gov server 3. **Phase 3: Testing & Quality Assurance** - Unit testing - Integration testing - Performance optimization 4. **Phase 4: Deployment & Distribution** - Package creation (PyPI, NPM, Docker) - Deployment documentation - Release management 5. **Phase 5: Maintenance & Enhancement** - Bug fixes - Feature enhancements - Community support ## Configuration Management ### Global Settings A centralized configuration system will manage: - **Database Connection Strings**: For data storage - **API Endpoints**: Source API URLs - **Concurrent Connections**: Performance tuning - **Rate Limiting**: API request throttling - **Logging Configuration**: Log levels and destinations ### Environment Support - **Development**: Local testing environment - **Staging**: Pre-production testing - **Production**: Live deployment - **Custom**: User-defined environments ## Security Considerations ### Security Measures - **No Hardcoded Secrets**: All credentials in config files - **Environment Variables**: Secure secret management - **Data Encryption**: For sensitive information - **Access Control**: Role-based permissions - **Audit Logging**: Comprehensive activity logs ### Compliance - **Government Data Policies**: Proper data usage - **Open Source Licensing**: License compliance - **Privacy Regulations**: Data protection ## Performance Requirements ### Target Metrics - **Throughput**: 10,000+ documents/hour - **Concurrency**: 50+ simultaneous connections - **Memory Usage**: < 2GB for typical workloads - **Response Time**: < 500ms for MCP calls ### Optimization Strategies - **Batch Processing**: Efficient data handling - **Memory Management**: Garbage collection tuning - **Connection Pooling**: Database optimization - **Caching**: Frequent data caching ## Distribution Strategy ### PyPI Package - **Package Name**: `opendiscourse-congress` & `opendiscourse-govinfo` - **Python Version**: 3.8+ - **Dependencies**: Minimal core dependencies ### NPM Package - **Package Name**: `@opendiscourse/congress` & `@opendiscourse/govinfo` - **Node Version**: 14+ - **TypeScript Support**: Full type definitions ### Docker Containers - **Image Tags**: Latest, version-specific - **Multi-arch Support**: x86_64, ARM - **Size Optimization**: Minimal image sizes ### Direct Installation - **Source Code**: GitHub repository - **Installation Scripts**: Automated setup - **Configuration Templates**: Example configs ## Documentation Plan ### Documentation Deliverables 1. **API Documentation**: Complete tool specifications 2. **Installation Guides**: Setup instructions 3. **User Manuals**: Usage examples 4. **Developer Guides**: Extension points 5. **Troubleshooting**: Common issues ### Documentation Formats - **Markdown**: Source documentation - **HTML**: Web-based documentation - **PDF**: Printable guides - **Interactive**: Online documentation ## Risk Assessment ### Potential Risks 1. **API Changes**: Government API modifications - *Mitigation*: Versioned API support, change detection 2. **Data Volume**: Large dataset processing - *Mitigation*: Batch processing, memory optimization 3. **Rate Limiting**: API request restrictions - *Mitigation*: Configurable throttling, retry logic 4. **Security Vulnerabilities**: Credential exposure - *Mitigation*: Secure config management, audits ## Success Criteria ### Project Success Metrics - ✅ Two fully functional MCP servers - ✅ Successful PyPI and NPM package publication - ✅ Docker images available and functional - ✅ Comprehensive documentation completed - ✅ 95%+ test coverage achieved - ✅ Production deployment capability - ✅ Community adoption and usage ## Timeline (Estimated) | Phase | Duration | Target Completion | |-------|----------|-------------------| | Planning | 2 weeks | 2025-12-25 | | Development | 8 weeks | 2026-02-20 | | Testing | 4 weeks | 2026-03-20 | | Deployment | 2 weeks | 2026-04-03 | | Maintenance | Ongoing | - | ## Team & Responsibilities ### Core Team - **Project Lead**: Overall coordination - **Python Developers**: Core implementation - **TypeScript Developers**: NPM package - **DevOps Engineers**: Deployment pipelines - **QA Engineers**: Testing and validation - **Documentation Specialists**: Documentation creation ### Community Contributions - **Open Source**: Accepting community contributions - **Issue Tracking**: GitHub issue management - **Feature Requests**: Community-driven enhancements ## Next Steps 1. **Finalize Documentation**: Complete all markdown files 2. **Create Global Settings**: Implement configuration system 3. **Begin Core Development**: Start with shared components 4. **Setup CI/CD Pipelines**: Automated testing and deployment 5. **Community Outreach**: Build user community ## Conclusion The OpenDiscourse MCP Servers project represents a comprehensive effort to provide standardized access to U.S. government data through modern MCP interfaces. By creating two separate but complementary servers, this project will enable developers, researchers, and AI systems to easily access and process vast amounts of congressional and government publication data. The modular architecture, multiple distribution channels, and production-ready design ensure that these tools will be valuable assets for anyone working with government data in AI and analytical applications.